International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.6.4
Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878
Geospatial Technology Based Rainfall Precipitation Assessment with Landslides in Mettupalayam – Aravankadu Highway, Tamilnadu Ganesh R1, Gowtham B1, Manivel T2 1
Department of Geology, Presidency College (Autonomous), Chennai, India Department of Earth Science, Annamalai University, Chidambaram, India
2
Abstract— The present study reveals that the relation between rainfall Precipitation with landslides was carried out. The Precipitation data were collected from IWS (Institution of Water Studies) and analyzed for annual and season wise for the period from 2006 to 2015. The Precipitation data were interpret tolated through spatial distribution methods in GIS and correlated with existing landslide locations. The spatial output of rainfall contour shows that larger area of rainfall is covered with higher amount in Northeast Monsoon when compared to other seasons. However, an almost equal amount of rainfall was noticed in Southwest Monsoon. The above data were taken into a GIS. Using this data, spatial interpolation maps were prepared. It clearly reveals that, high amount of rainfall and existence of landslides occurs throughout the Coonoor region and Wellington and Moderate amount of rainfall and existence of landslides in Kothagiri and Ooty region. This paper highlights the application of GIS in spatially locating the relation between precipitation and landslides. Keywords— Geospatial Technology, Aravankadu Highway, IWS. I. INTRODUCTION A landslide is an event of nature that leads to sudden disruption of normal life of society, causing damage to property of nations, to such an extent those normal, social and economic mechanisms available are inadequate to restore normalcy. Landslides are defined as the mass movement of rocks, debris or earth along a sliding plane. They are characterised by almost permanent contact between the moving masses and sliding plane (Butler, 1976; Crozier, 1984; and Smith, 1996). Landslides cause substantial economic, human and environmental losses throughout the world. Examples of devastating landslides at a global scale include the 1972 Calabria landslide in Italy, the 1970 www.ijeab.com
Hauscaran landslide in Peru (McCall, 1992), the 1966 Aberfan landslide in wales, and the 1985 Armero landslide in Colombia (Alexander, 1993). It is estimated that in 1998, 180,000 avalanches, landslides, and debris flow in different scales occurred in China, estimated at 3 billion dollars’ worth of direct economic losses (Huabin et al., 2005). II. STUDY AREA The study area is the Nilgiris district, which is located in Tamilnadu state. The Mettupalaym to Aravankadu ghat section of length 273.30 km2 has taken as the study area to identify the landslide prone areas. It lies in the toposheet Nos. 58 A/15 of survey of India and located in between 76° 48’ 8.34’’ and 76°54’ 2.48’’ E longitudes and 11° 17’ 41.25’’ and 11° 17’ 47.48’’ N latitudes with an area of (273.30 km2 ). The study area is blessed with deltaic system with different active and inactive distributaries and shown in figure 1. The proposed study area is covered include villages like Mettupalayam, Odanthurai, Adatturai, Burliyar, Hulical Drug, Kallar, Killpilur, Marrapalam, wellington, Aravankadu, Lambs rock and Tiger hill. III. METHODOLOGY The base map is prepared from Survey of India (SOI) Toposheets 58A/11 &15 at a scale of 1: 50000. In the present study, the average monthly rainfall of a ten years period (2006 - 2015) have been collected from five rain gauge stations and variation diagrams are prepared. Rainfall contour map has been prepared of rainfall variation is found at all the rain gauge stations. The spatial variability of mean annual precipitation depends upon the topographic factors such as exposure of station to the prevailing wind, elevation, orientation and slope of the mountain (Basist A and Bell G.D., 1994). Page | 2798
International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.6.4
Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878
Fig.1: Study Area Base Map Arithmetic mean is used for measurements of selected duration at all rain gauges are summed and the total divided by the number of gauges. Arithmetic method is the simplest objective methods of calculating the average rainfall over the area (Basavarajappa et al., 2015a). Thiessen polygon method provides the individual areas of influence around each set of points. Thiessen (1911), an American engineer adopted the polygon method for rainfall measurements at individual gauges as first weighted by the fractions of the catchment area represented by the gauges, and then summed. Thiessen polygons are the polygons whose boundaries are mathematically define the area (perpendicular bisectors) that is closest to each point relative to all other points (Basavarajappa et al., 2015 a). Iso-hyetal method is a line drawn on a map connecting points that receive equal amounts of rainfall. It is one of the convenient methods that views continuous spatial variation of rainfall areas. The main aim of the method, to draw lines of equal rainfall amount (isohyets) using observed amounts at stations (Reed W.G and Kincer J.B., 1917). In iso-hyetal map, the x-axis represents East Longitude, while y-axis represents North Latitude (Basavarajappa et al., 2015 a).
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IV. RESULTS AND DISCUSSION The results of post-monsoon, pre-monsoon, southwest, northeast and average annual rainfall data for the period 2006- 2015 were used for the preparation of spatial distribution contour map using geospatial technology and the data’s are given in figures 2 to 11 and in table.1. Pre monsoon Season During the pre-monsoon season, study area recorded an average rainfall of 453.83 mm. During this Season, the highest rainfall of 148.78 mm was recorded in Runneymedu station and the lowest rainfall of 43.79 mm was recorded in Gurrency station. Post monsoon Season During the post monsoon season, study area recorded an average rainfall of 1231.04 mm. During this Season, the highest rainfall of 347.22 mm was recorded in Coonoor station and the lowest rainfall of 162.43 mm was recorded in Gurrency station. South-West Monsoon Season During the South–West Monsoon season, study area recorded an average rainfall of 1435.73 mm. During this Season, the highest rainfall of 403.19 mm was recorded in Hilgrove station and the lowest rainfall of 236.31 mm was recorded in Aderly station. Page | 2799
International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.6.4 North-East Monsoon Season During the North-East Monsoon season, study area recorded an average rainfall of 2934.07 mm. During this
Stations COONOOR
Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878
Season, the highest rainfall of 953.93 mm was recorded in Coonoor station and the lowest rainfall of 351.38 mm was recorded in Mettupalayam station.
Table.1: Average occurrences of rainfall during various seasons PostPreSW Average NE monsoon monsoon monsoon monsoon Rainfall 72 368 297.4 1430.6 542
RUNNEYMEDU
56
392
346
1292
521.5
HILGROVE
103
397.6
454
625
394.9
GURRENCY
74
450.8
372
1094
497.7
ADERLY
52
312
198
481
260.75
METTUPALAYAM
7
277
107.9
571
240.725
31.6
131.3
2038.1
662.8
715.95
RUNNEYMEDU
85
143.8
773.8
350.9
338.375
HILGROVE
166
348
1714.2
413.4
660.4
GURRENCY
15.8
54
1470
519
514.7
ADERLY
8
206.1
768.2
528.4
377.675
METTUPALAYAM
33
96
173
314
154
352.9
556.5
492.1
509
477.625
499
472
178.6
547.2
424.2
HILGROVE
291.6
329.4
133.7
598
338.175
GURRENCY
136.4
455.8
125
580.2
324.35
236
316
172
422.6
286.65
COONOOR
COONOOR RUNNEYMEDU
ADERLY METTUPALAYAM
44
286
264
300
223.5
10.2
190.9
371.4
1509.3
520.45
RUNNEYMEDU
0
284
264
1473
505.25
HILGROVE
0
257
251
541
262.25
GURRENCY
0
200
142.7
106.4
112.275
ADERLY
0
172
116.1
84.2
93.075
METTUPALAYAM
0
249
153
378.7
195.175
41.6
138.1
342.8
897.9
355.1
COONOOR
COONOOR RUNNEYMEDU
32
96
161.3
1551
460.075
HILGROVE
7
117.5
134
709
241.875
GURRENCY
4
14.8
104.8
438.6
140.55
ADERLY
4.3
44.7
111.6
147.2
76.95
METTUPALAYAM
16
159.2
209.20
603.90
247.08
COONOOR
654.8
262.5
427.5
1239.3
646.025
RUNNEYMEDU
682.8
161.4
273.5
1100.4
554.525
HILGROVE
762.8
174.8
342
884
540.9
GURRENCY
175.7
37
95.4
248.8
139.225
ADERLY
27.2
24.3
24.2
393.1
117.2
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Year
2006
2007
2008
2009
2010
2011
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International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.6.4 Postmonsoon 235
Premonsoon 191.30
SW monsoon 75.90
COONOOR
14
214
RUNNEYMEDU
0
HILGROVE
Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878
NE monsoon
Average Rainfall
593.70
273.97
206.6
925.10
339.93
264
194
698.6
289.15
0
196
161
650.2
251.80
GURRENCY
0
128.9
309
625
265.73
ADERLY
5
175.5
294.4
443.5
229.60
METTUPALAYAM
18.40
123.70
126.50
359.60
157.05
COONOOR
45.80
370.20
303.20
417.70
284.22
RUNNEYMEDU
39.00
281.00
211.00
554.00
271.25
HILGROVE
20.00
305.00
140.00
650.20
278.80
GURRENCY
32.00
283.00
33.20
0.00
87.05
ADERLY
36.00
255.50
222.00
501.00
253.63
METTUPALAYAM
63.10
102.40
78.40
393.00
159.22
COONOOR
108.20
283.40
277.60
913.20
395.60
RUNNEYMEDU
94.00
299.00
486.40
1061.00
485.10
HILGROVE
100.00
406.00
403.00
1080.00
497.25
GURRENCY
0.00
0.00
0.00
0.00
0.00
ADERLY
107.50
401.00
225.10
648.80
345.60
METTUPALAYAM
46.40
185.30
0.00
0.00
57.92
COONOOR
9.00
957.30
402.00
1034.40
600.68
RUNNEYMEDU
0.00
800.50
374.00
774.00
487.13
HILGROVE
0.00
500.00
299.00
818.30
404.33
GURRENCY
0.00
0.00
0.00
0.00
0.00
ADERLY
0.00
305.50
231.50
604.00
285.25
METTUPALAYAM
0.00
0.00
0.00
0.00
0.00
Stations METTUPALAYAM
Year
2012
2013
2014
2015
PRE MONSOON
POST MONSOON
PRE MONSOON
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
2500
1600 1400 1200 1000 800 600 400 200 0
2000 1500 1000 500 0
Fig.: 2 Annual Rainfall (in mm) 2006
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Fig.: 3 Annual Rainfall (in mm) 2007
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International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.6.4 PRE MONSOON
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
700 600 500 400 300 200 100 0
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
1600 1400 1200 1000 800 600 400 200 0
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
1800 1600 1400 1200 1000 800 600 400 200 0
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
Fig.: 7 Annual Rainfall (in mm) 2011 PRE MONSOON
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
1000 900 800 700 600 500 400 300 200 100 0
Fig.: 8 Annual Rainfall (in mm) 2012
Fig.: 5 Annual Rainfall (in mm) 2009 PRE MONSOON
PRE MONSOON
1400 1200 1000 800 600 400 200 0
Fig.: 4 Annual Rainfall (in mm) 2008 PRE MONSOON
Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878
PRE MONSOON
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
700 600 500 400 300 200 100 0
Fig.: 6 Annual Rainfall (in mm) 2010 Fig.: 9 Annual Rainfall (in mm) 2013
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Page | 2802
International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.6.4 PRE MONSOON
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878
1200 1000 800 600 400 200 0
Fig.: 10 Annual Rainfall (in mm) 2014 Fig.: 13 Rainfall contour Post monsoon PRE MONSOON
POST MONSOON
SOUTH WEST MONSOON
NORTH EAST MONSOON
1200 1000 800 600 400 200 0
Fig.: 11 Annual Rainfall (in mm) 2015
Fig.: 14 Rainfall contour south west monsoon
Fig.: 15 Rainfall contour north east monsoon Fig.: 12 Rainfall contour Pre monsoon www.ijeab.com
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International Journal of Environment, Agriculture and Biotechnology (IJEAB) http://dx.doi.org/10.22161/ijeab/2.6.4 It is observed from the figures 12 to 15 that the isohyetal maps of pre-monsoon is lesser than post monsoon. In these maps, the increasing order of intensity is noticed towards the coonoor rain gauge station. However, in NE monsoon isohyetal map shows that scenario is quite high with compare to other monsoons. Though the contribution of pre monsoon rainfall is too low, the intensity increases towards NE in the basin. The rainfall is considered as one of the prime triggering mechanism. Mostly the landslides that are happening in this region as triggered by rainfall so as in any landslides in Tamilnadu. The high hills with steep slopes are controlled by newer evolution of the plateaus probably tectonic plateaus, bounded by N S- N E and SW lineaments. These differently oriented lineaments make the slope of the plateaus very much vulnerable to landslides. Moreover the plateaus are highly dissected, may be as a result of cumulative effects of all the tectonic events in this region. Higher degree of deformation and recrystallization makes the rocks break easily and ready to slide.
Vol-2, Issue-6, Nov-Dec- 2017 ISSN: 2456-1878
on the San Andreas Fault, to be submitted to Bull. Seism. Soc. Am. [5] Crozier M.J.: 1984, Field Assessment of Slope Instability in D Brunsden and D Prior (eds), Slope Instability, , New York, John Wiley and Sons. [6] Huabin, W., Gangjun, L., Weiya, X., & Gunghui, W. 2005. GIS-based landslide hazard assessment: an overview. Progress in physical geography, 29(4):548567. [Online] Available: ppg.sagepub.com [3 June 2011]. [7] McCall, G.J.H. (1992) Geohazards Natural and ManMade, London. Chapman and Hall. [8] Reed W.G and Kincer J.B (1917). The preparation of precipitation chart, Monthly Weather Rev., Vol.45, Pp: 233-235. [9] Smith, K., 1996. Environmental Hazards: Assessing Risk and Reducing Disaster. Routledge, London, p. 478. [10] Thiessen A.H., (1911), Precipitation for large areas, Monthly Weather Revision, 39, pp 1082-1084.
V. CONCLUSION Monthly rainfall analysis concludes that the highest intensity of rain showers is recorded during the month of October, while the lowest intensity is usually recorded during January at all the six rain gauge stations. Analysis of seasonal rainfall concludes that the percentage contributions of rainfall during various monsoon periods are in the following order: NE monsoon (52.34%) > SW monsoon (23.66%) > Post-monsoon (16.70%) > Pre-monsoon (7.30%). Spatial distribution pattern of rainfall indicates that the intensity of rainfall increases towards NE monsoon. Lesser intensity was found in pre-monsoon with compare post-monsoon season. REFERENCES [1] Alexander, D. (1993) Natural Disaster, London, University College Library Press. [2] Basavarajappa H.T, Manjunatha M.C and Pushpavathi K.N (2015a). Mapping and Reclamation of wastelands through Geomatics technique in Precambrian terrain of Mysuru district, Karnataka, India., International Journal of Civil and Structural Engineering (IJCSE), Vol.5, No.4, Pp: 379-391. [3] Basist A and Bell G.D., (1994), Statistical relationships between topography and precipitation patterns, Journal of Climate, 7, pp 1305-1315. [4] Butler, R. and H. Kanamori (1976). Long-period ground motion in Los Angeles from a great earthquake www.ijeab.com
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